Learning on real robots from experience and simple user feedback
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Título: | Learning on real robots from experience and simple user feedback |
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Autor/es: | Quintía Vidal, Pablo | Iglesias Rodríguez, Roberto | Rodríguez González, Miguel Ángel | Vázquez Regueiro, Carlos |
Palabras clave: | Autonomous robots | Reinforcement learning |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | ene-2013 |
Editor: | Red de Agentes Físicos |
Cita bibliográfica: | QUINTÍA, P., et al. “Learning on real robots from experience and simple user feedback”. Journal of Physical Agents. Vol. 7, No. 1 (Jan. 2013). ISSN 1888-0258, pp. 57-65 |
Resumen: | In this article we describe a novel algorithm that allows fast and continuous learning on a physical robot working in a real environment. The learning process is never stopped and new knowledge gained from robot-environment interactions can be incorporated into the controller at any time. Our algorithm lets a human observer control the reward given to the robot, hence avoiding the burden of defining a reward function. Despite the highly-non-deterministic reinforcement, through the experimental results described in this paper, we will see how the learning processes are never stopped and are able to achieve fast robot adaptation to the diversity of different situations the robot encounters while it is moving in several environments. |
Patrocinador/es: | This work was supported by the research grant TIN2009-07737 of the Spanish Ministerio de Economía y Competitividad, and María Barbeito program of the Xunta de Galicia. |
URI: | http://hdl.handle.net/10045/26263 | http://dx.doi.org/10.14198/JoPha.2013.7.1.08 |
ISSN: | 1888-0258 |
DOI: | 10.14198/JoPha.2013.7.1.08 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | Creative Commons License Attribution-ShareAlike 3.0 |
Revisión científica: | si |
Aparece en las colecciones: | Journal of Physical Agents - 2013, Vol. 7, No. 1 |
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